The above equation provides us a natural way to transform the classification head of the standard Faster R-CNN [1]} to an energy function. Due to the clear separation that we enforce in the latent space with the contrastive clustering, we see a clear separation in the energy level of the known class data-points and unknown data-points as illustrated in Fig. REF . In light of this trend, we model the energy distribution of the known and unknown energy values \(\xi _{kn}(\mathbf {f})\)  and \(\xi _{unk}(\mathbf {f})\) , with a set of shifted Weibull distributions. These distributions were found to fit the energy data of a small held out validation set (with both knowns and unknowns instances) very well, when compared to Gamma, Exponential and Normal distributions. The learned distributions can be used to label a prediction as unknown if \(\xi _{kn}(\mathbf {f}) < \xi _{unk}(\mathbf {f})\) .
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